Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Chiller system performance management with market basket analysis

Chiller system performance management with market basket analysis This study aims to apply association rule mining (ARM) to uncover specific associations between operating components of a chiller system and improve its coefficient of performance (COP), hence reducing the electricity use of buildings with central air conditioning.Design/methodology/approachFirst, 13 operating variables were identified, comprising measures of temperatures and flow rates of system components and their switching statuses. The variables were grouped into four bins before carrying out ARM. Strong rules were produced to associate the variables and switching statuses with different COP classes.FindingsThe strong rules explain existing constraints on practising chiller sequencing and prioritise variables for optimisation. Based on strong rules for the highest COP class, the optimal operating strategy involves rescheduling chillers and their associated components in pairs during a high load operation. Resetting the chilled water supply temperature is the next best strategy, followed by resetting the condenser water entering temperature, subject to operating constraints.Research limitations/implicationsThis study considers the even frequency method with four bins only. Replication work can be done with other discretisation methods and different numbers of classes to compare potential differences in the bin ranges of the optimised variables.Practical implicationsThe strong rules identified by ARM highlight associations between variables and high or low COPs. This supports the selection of critical variables and the operating status of system components to maximise the COP. Tailor-made optimisation strategies and the associated electricity savings can be further evaluated.Originality/valuePrevious studies applied ARM for chiller fault detection but without considering system performance under the interaction of different components. The novelty of this study is its demonstration of ARM’s intelligence at discovering associations in past operating data. This enables the identification of tailor-made energy management opportunities, which are essential for all engineering systems. ARM is free from the prediction errors of typical regression and black-box models. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Facilities Emerald Publishing

Chiller system performance management with market basket analysis

Ho, Wai Tung; Yu, Fu Wing
Facilities , Volume 39 (9/10): 21 – Jun 2, 2021
Download PDF
21 pages

Loading next page...
 
/lp/emerald-publishing/chiller-system-performance-management-with-market-basket-analysis-cVzQ0LWJtf
Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
0263-2772
DOI
10.1108/f-09-2020-0107
Publisher site
See Article on Publisher Site

Abstract

This study aims to apply association rule mining (ARM) to uncover specific associations between operating components of a chiller system and improve its coefficient of performance (COP), hence reducing the electricity use of buildings with central air conditioning.Design/methodology/approachFirst, 13 operating variables were identified, comprising measures of temperatures and flow rates of system components and their switching statuses. The variables were grouped into four bins before carrying out ARM. Strong rules were produced to associate the variables and switching statuses with different COP classes.FindingsThe strong rules explain existing constraints on practising chiller sequencing and prioritise variables for optimisation. Based on strong rules for the highest COP class, the optimal operating strategy involves rescheduling chillers and their associated components in pairs during a high load operation. Resetting the chilled water supply temperature is the next best strategy, followed by resetting the condenser water entering temperature, subject to operating constraints.Research limitations/implicationsThis study considers the even frequency method with four bins only. Replication work can be done with other discretisation methods and different numbers of classes to compare potential differences in the bin ranges of the optimised variables.Practical implicationsThe strong rules identified by ARM highlight associations between variables and high or low COPs. This supports the selection of critical variables and the operating status of system components to maximise the COP. Tailor-made optimisation strategies and the associated electricity savings can be further evaluated.Originality/valuePrevious studies applied ARM for chiller fault detection but without considering system performance under the interaction of different components. The novelty of this study is its demonstration of ARM’s intelligence at discovering associations in past operating data. This enables the identification of tailor-made energy management opportunities, which are essential for all engineering systems. ARM is free from the prediction errors of typical regression and black-box models.

Journal

Facilities – Emerald Publishing

Published: Jun 2, 2021

Keywords: Optimisation techniques; Efficiency; Energy management; Energy saving; Energy consumption; Air-conditioning; Association rule mining; Coefficient of performance; Market basket analysis; Water-cooled chiller

References

  • Mining association rules between sets of items in large databases
    Agrawal, R.; Imielinski, T.; Swami, A.
  • Computational intelligence techniques for HVAC systems: a review
    Ahmad, M.W.; Mourshed, M.; Yuce, B.; Rezgui, Y.
  • AHRI standard 550/590: ‘performance rating of water-chilling and heat pump water-heating packages using the vapor compression cycle’
  • Adaptive modeling for reliability in optimal control of complex HVAC systems
    Asad, H.S.; Yuen, R.K.K.; Liu, J.; Wang, J.
  • ANSI/ASHRAE/IES standard 90.1-2019
  • FIMI’03: Workshop on frequent itemset mining implementations
    Borgelt, C.
  • Induction of association rules: apriori implementation
    Borgelt, C.; Kruse, R.
  • Exergoeconomic analysis as support in decision-making for the design and operation of multiple chiller systems in air conditioning applications
    Catrini, P.; Piacentino, A.; Cardona, F.; Ciulla, G.
  • Application of artificial neural network and genetic algorithm to the optimization of load distribution for a multiple-type-chiller plant
    Chan, T.; Chang, Y.; Huang, J.
  • Data mining algorithm and framework for identifying HVAC control strategies in large commercial buildings
    Chen, Z.; Xu, P.; Feng, F.; Qiao, Y.; Luo, W.
  • Historian
  • The building energy efficiency ordinance
  • Technical guidelines on retro-commissioning
  • Unsupervised data analytics in mining big building operational data for energy efficiency enhancement: a review
    Fan, C.; Xiao, F.; Li, Z.D.; Wang, J.Y.
  • The relationship between energy use and space use of higher educational buildings in subtropical Australia
    Gui, X.C.; Gou, Z.H.; Zhang, F.
  • Arules – a computational environment for mining association rules and frequent item sets
    Hahsler, M.; Grun, B.; Hornik, K.
  • BEAM Plus
  • An effective fault diagnosis method for centrifugal chillers using associative classification
    Huang, R.G.; Liu, J.Y.; Chen, H.X.; Li, Z.F.; Liu, J.H.; Li, G.N.; Guo, Y.B.; Wang, J.Y.
  • Fault and sensor error diagnostic strategies for a vapor compression refrigeration system by using fuzzy inference systems and artificial neural network
    Kocyigit, N.
  • Development of a user-friendly regression model to evaluate carbon emissions of office buildings design in the subtropics
    Lee, P.; Chan, E.W.H.; Qian, Q.K.; Lam, P.T.I.
  • Data partitioning and association mining for identifying VRF energy consumption patterns under various part loads and refrigerant charge conditions
    Li, G.N.; Hu, Y.P.; Chen, H.X.; Li, H.R.; Sun, M.
  • The analysis of the operating performance of a chiller system based on hierarchal cluster method
    Li, M.; Ju, Y.
  • A critical review of fault modeling of HVAC systems in buildings
    Li, Y.; O’Neill, Z.
  • Data-driven and association rule mining-based fault diagnosis and action mechanism analysis for building chillers
    Liu, J.Y.; Shi, D.L.; Li, G.N.; Xie, Y.; Li, K.N.; Liu, B.; Ru, Z.P.
  • An algorithmic approach for mining customer behavior prediction in market basket analysis
    Maske, A.R.; Joglekar, B.; Saini, H.; Sayal, R.; Govardhan, A.; Buyya, R.
  • Modeling and optimization of HVAC systems using artificial neural network and genetic algorithm
    Nassif, N.
  • Data mining based framework to identify rule based operation strategies for buildings with power metering system
    Qiu, S.; Feng, F.; Li, Z.; Yang, G.; Xu, P.; Li, Z.
  • R: a language and environment for statistical computing
  • A steady-state empirical model for evaluating energy efficient performance of centrifugal water chillers
    Wang, H.D.
  • A practical chiller fault diagnosis method based on discrete bayesian network
    Wang, Y.L.; Wang, Z.W.; He, S.W.; Wang, Z.W.
  • Fault detection for centrifugal chillers using a kernel entropy component analysis (KECA) method
    Xia, Y.; Ding, Q.; Li, Z.; Jiang, A.
  • Abnormal energy consumption detection for GSHP system based on ensemble deep learning and statistical modeling method
    Xu, C.L.; Chen, H.X.
  • Energy management of chiller systems by data envelopment analysis
    Yu, F.W.; Chan, K.T.
  • Review of standards for energy performance of chiller systems serving commercial buildings
    Yu, F.W.; Chan, K.T.; Sit, R.K.Y.; Yang, J.
  • An improved association rule mining-based method for revealing operational problems of building heating, ventilation and air conditioning (HVAC) systems
    Zhang, C.B.; Xue, X.; Zhao, Y.; Zhang, X.J.; Li, T.T.
  • An online predictive control method with the temperature based multivariable linear regression model for a typical chiller plant system
    Zhao, T.; Wang, J.; Xu, M.; Li, K.
  • A review of data mining technologies in building energy systems: load prediction, pattern identification, fault detection and diagnosis
    Zhao, Y.; Zhang, C.B.; Zhang, Y.W.; Wang, Z.H.; Li, J.Y.
  • Development and implementation of a virtual outside air wet-bulb temperature sensor for improving water-cooled chiller plant energy efficiency
    Zheng, K.K.; Watt, J.; Wang, C.; Cho, Y.K.
  • An operational parameter optimization method based on association rules mining for chiller plant
    Zhou, X.; Wang, B.W.; Liang, L.Q.; Yan, J.W.; Pan, D.M.
  • Hybrid model based refrigerant charge fault estimation for the data centre air conditioning system
    Zhu, X.; Du, Z.M.; Chen, Z.J.; Jin, X.Q.; Huang, X.Q.